import qlib
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

class QlibAnalyzer:
    """使用Microsoft Qlib进行量化分析"""
    
    def __init__(self, config):
        self.config = config
        # 初始化Qlib，设置数据源
        qlib.init(provider_uri=config.get('qlib_data_path', '~/.qlib/qlib_data/cn_data'))
        self.models = {}
    
    # 简化后的版本可能仅使用基本的数据访问功能
    def prepare_data(self, stock_code, start_date, end_date):
        """准备股票数据"""
        # 转换股票代码格式
        if '.' in stock_code:
            code_parts = stock_code.split('.')
            if code_parts[1] == 'SH':
                qlib_code = f"SH{code_parts[0]}"
            else:
                qlib_code = f"SZ{code_parts[0]}"
        else:
            qlib_code = stock_code
            
        # 获取特征数据
        from qlib.data import D
        fields = ['$open', '$high', '$low', '$close', '$volume']
        instruments = qlib_code
        
        try:
            data = D.features(instruments=instruments, 
                             fields=fields, 
                             start_time=start_date, 
                             end_time=end_date)
            return data, qlib_code
        except Exception as e:
            print(f"获取Qlib数据失败: {str(e)}")
            # 返回一个空的DataFrame和代码
            return pd.DataFrame(), qlib_code
    
    def get_market_analysis(self, stock_code, days=30):
        """获取市场分析结果"""
        # 简化版本直接返回基本分析结果
        return {
            'stock_code': stock_code,
            'current_price': 0.0,
            'trend': '无法分析，Qlib模块导入错误',
            'predicted_return_1d': 0.0,
            'predicted_return_5d': 0.0
        }